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Self-calibration of a 1D projective camera and its application to the self-calibration of a 2D projective camera

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1406)


We introduce the concept of self-calibration of a 1D projective camera from point correspondences, and describe a method for uniquely determining the two internal parameters of a 1D camera based on the trifocal tensor of three 1D images. The method requires the estimation of the trifocal tensor which can be achieved linearly with no approximation unlike the trifocal tensor of 2D images, and solving for the roots of a cubic polynomial in one variable. Interestingly enough, we prove that a 2D camera undergoing a planar motion reduces to a 1D camera. From this observation, we deduce a new method for self-calibrating a 2D camera using planar motions.

Both the self-calibration method for a 1D camera and its applications for 2D camera calibration are demonstrated on real image sequences. Other applications including 2D affine camera self-calibration are also discussed.


  • Planar Motion
  • Internal Parameter
  • Optical Center
  • Principal Point
  • Point Correspondence

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© 1998 Springer-Verlag Berlin Heidelberg

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Faugeras, O., Quan, L., Sturm, P. (1998). Self-calibration of a 1D projective camera and its application to the self-calibration of a 2D projective camera. In: Burkhardt, H., Neumann, B. (eds) Computer Vision — ECCV'98. ECCV 1998. Lecture Notes in Computer Science, vol 1406. Springer, Berlin, Heidelberg.

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  • Print ISBN: 978-3-540-64569-6

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